Tidy Tuesday Submission
Kelsey Matsik
2024-04-28
Data Summary
Read about the data, outer_space_objects.csv: https://github.com/rfordatascience/tidytuesday/tree/master/data/2024/2024-04-23
This data set contains information on the number of objects launched in space by different entities and countries from 1956 to 2023. “Objects” refers to satellites, probes, landers, crewed spacecrafts, and space station flight elements launched into or beyond Earth’s orbit. The data was collected by the United Nations Office for Outer Space Affairs in 2024 and cleaned by Our World in Data. The data contains four variables: Entity (entity, country, or other agency/organization), Code (entity code), Year, or num_objects (annual number of objects launched into outer space).
For each of my visualizations, I created a cum_num_objects column, which added the number of space objects for each entity over the years in the data.
Description
For both of my visualizations, I chose to focus on the top countries/entities in order to minimize clutter. Also, though all entities in the data set contribute to showing the relative increase in space exploration over the last 70 years, the top countries and entities show the most dramatic and observable increase. In Viz 1, “top 10 countries” refers to the top 10 countries that have launched the most space objects from 1957-2023. In Viz 2, “top 8 agencies” refers to the top 8 entities, such as government or private organizations, that have launched the most space objects from 1957-2023.
Viz 1 is a Shiny App that shows the top 10 countries’ total number of objects in space over a specified range of years. The app contains a slider input which allows the user to toggle between different year ranges with the default range set from 1957 to 2023 (all years in the data). This viz is effective because it allows the viewer to see the the increase in total space objects over the past 70 years as well as the how the top 10 countries have increased their numbers of space objects. More specifically, the viewer can see the time at which the number of objects in space exponentially increased and which countries primarily drove that change. The viewer can set different year ranges to zoom in or zoom out on the data and contextualize trends of relative stability or increase of the total number of space objects.
Around 2017, the total space objects began to exponentially increase. Looking at the fill of the bars, India is the country that appeared to drive the drastic uptick. From 2017 to 2023, the total number of objects in space launched by the top 10 countries more than doubled; in 2017, less than 7,500 objects were launched in space, but by 2023, this number rose to over 15,000.
Viz 2 is an interactive line plot which shows the total number of space objects launched by the top 8 entities that are not countries over the past 70 years. Comparing the number of space objects launched by countries versus other entities, such as government or private organizations, is important in understanding who is primarily driving human space exploration. Viz 2 reveals that while some organizations have had constant to little no increase in the number of objects they’ve launched, others have participated in dramatic increases. Looking at the plot, Intelstat and the European Space Agency launched similar numbers of objects in space until 2004, when Intelsat’s growth leveled off and European Space Agency continued to increase, having launched over 40 more space objects than Intelsat today.
This viz is effective for three reasons. First, the time series design allows the user to see the relative increases of entities’ participation in space exploration from 1957 to 2023. Second, interactive features allow the user to hover over a line and see the year, entity, and number of space objects they’d launched, minimizing visual load by elminating the need to look between the data, legend, and axes. Third, the aesthetic properties of the plot, such as color, font size, axis labels, and line plot with data points are informative and effective in communicating information without overwhelming the user.
Appendix
The appendix includes all code used to perform exploratory data analysis and create the visualizations.
library(dplyr)
library(plotly)
library(ggplot2)
library(viridis)df <- read.csv("~/Desktop/STAT 3280/Tidy Tuesday/outer_space_objects.csv")
head(df)## Entity Code Year num_objects
## 1 APSCO 2023 1
## 2 Algeria DZA 2002 1
## 3 Algeria DZA 2010 1
## 4 Algeria DZA 2016 3
## 5 Algeria DZA 2017 1
## 6 Angola AGO 2017 1table(df$Entity)##
## Algeria Angola APSCO
## 4 2 1
## Arabsat Argentina Armenia
## 11 16 1
## Australia Austria Azerbaijan
## 22 2 3
## Bangladesh Belarus Belgium
## 2 4 6
## Bhutan Bolivia Brazil
## 1 1 25
## Bulgaria Canada Chile
## 4 39 5
## China Colombia Costa Rica
## 48 3 1
## Czechia Denmark Djibouti
## 9 10 1
## Ecuador Egypt Estonia
## 1 8 3
## Ethiopia EUMETSAT European Space Agency
## 1 11 39
## European Union Eutelsat Finland
## 6 21 7
## France Germany Ghana
## 41 41 1
## Greece Guatemala Hungary
## 4 1 3
## India Indonesia Inmarsat
## 39 20 9
## Intelsat Intersputnik Iran
## 34 1 8
## Ireland Israel Italy
## 1 22 32
## Japan Jordan Kazakhstan
## 53 1 5
## Kenya Kuwait Laos
## 1 1 1
## Latvia Lithuania Luxembourg
## 1 6 26
## Malaysia Mauritius Mexico
## 8 1 14
## Moldova Monaco Mongolia
## 1 1 1
## Morocco NATO Nepal
## 3 5 1
## Netherlands New Zealand Nigeria
## 8 5 3
## North Korea Norway Pakistan
## 3 13 5
## Papua New Guinea Paraguay Peru
## 2 1 3
## Philippines Poland Portugal
## 5 8 1
## Qatar RASCOM Romania
## 1 2 1
## Russia Rwanda Saudi Arabia
## 67 2 7
## Sea Launch Singapore Slovakia
## 1 8 2
## Slovenia South Africa South Korea
## 2 7 18
## Spain Sri Lanka Starsem
## 22 1 1
## Sweden Switzerland Taiwan
## 14 7 8
## Thailand Tunisia Turkey
## 14 1 16
## Turkmenistan Uganda Ukraine
## 1 1 8
## United Arab Emirates United Kingdom United States
## 14 38 66
## Uruguay Venezuela Vietnam
## 8 3 4
## World Zimbabwe
## 67 1table(df$Code)##
## AGO ARE ARG ARM AUS AUT AZE
## 142 2 14 16 1 22 2 3
## BEL BGD BGR BLR BOL BRA BTN CAN
## 6 2 4 4 1 25 1 39
## CHE CHL CHN COL CRI CZE DEU DJI
## 7 5 48 3 1 9 41 1
## DNK DZA ECU EGY ESP EST ETH FIN
## 10 4 1 8 22 3 1 7
## FRA GBR GHA GRC GTM HUN IDN IND
## 41 38 1 4 1 3 20 39
## IRL IRN ISR ITA JOR JPN KAZ KEN
## 1 8 22 32 1 53 5 1
## KOR KWT LAO LKA LTU LUX LVA MAR
## 18 1 1 1 6 26 1 3
## MCO MDA MEX MNG MUS MYS NGA NLD
## 1 1 14 1 1 8 3 8
## NOR NPL NZL OWID_WRL PAK PER PHL PNG
## 13 1 5 67 5 3 5 2
## POL PRK PRT PRY QAT ROU RUS RWA
## 8 3 1 1 1 1 67 2
## SAU SGP SVK SVN SWE THA TKM TUN
## 7 8 2 2 14 14 1 1
## TUR TWN UGA UKR URY USA VEN VNM
## 16 8 1 8 8 66 3 4
## ZAF ZWE
## 7 1table(df$Year)##
## 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972
## 2 3 3 3 3 5 3 5 6 5 8 5 8 11 11 9
## 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988
## 5 12 11 11 8 9 7 6 10 8 12 14 13 8 9 14
## 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## 11 19 19 20 18 15 21 23 20 24 23 24 18 20 19 17
## 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
## 16 20 21 26 23 23 27 31 36 38 28 30 41 48 40 32
## 2021 2022 2023
## 42 46 49# Getting countries with top 10 space objects
sorted_objects <- df[order(-df$num_objects), ]
top10 <- sorted_objects[1:10, ]
top10 ## Entity Code Year num_objects
## 1174 World OWID_WRL 2023 2664
## 1173 World OWID_WRL 2022 2478
## 1092 United States USA 2023 2166
## 1091 United States USA 2022 1939
## 1172 World OWID_WRL 2021 1813
## 1171 World OWID_WRL 2020 1274
## 1090 United States USA 2021 1232
## 1089 United States USA 2020 984
## 1170 World OWID_WRL 2019 586
## 1168 World OWID_WRL 2017 456Viz 1 Code
# Sort data by unique entities with highest number of space objects
df_sorted <- df %>%
group_by(Entity) %>%
summarize(num_objects = n()) %>%
arrange(desc(num_objects)) # Get top 15 entities
top_entities <- df_sorted[1:15,]# Filter out entities that aren't countries
top_countries <- top_entities %>%
filter(!(Entity %in% c("World", "European Space Agency", "Intelsat")))
# Get only top 10 countries
top10_countries <- top_countries[1:10, ]
top10_countries## # A tibble: 10 × 2
## Entity num_objects
## <chr> <int>
## 1 Russia 67
## 2 United States 66
## 3 Japan 53
## 4 China 48
## 5 France 41
## 6 Germany 41
## 7 Canada 39
## 8 India 39
## 9 United Kingdom 38
## 10 Italy 32# Create df with only top 10 countries who have the most total space objects
top10df <- df %>%
filter(Entity %in% top10_countries$Entity)# Get cumulative number of objects in space over the years for each entity
top10df <- top10df %>%
arrange(Year) %>%
group_by(Entity) %>%
mutate(cum_num_objects = cumsum(num_objects))
# top10df# Export data to be used in Shiny App
write.csv(top10df, "~/Desktop/STAT 3280/Tidy Tuesday/top10_countries.csv")# Original plot to be used in Shiny App
p1 <- ggplot(top10df, aes(x = Year, y = cum_num_objects, fill = Entity)) +
geom_bar(stat = "identity") +
scale_fill_viridis_d() +
theme_minimal() +
labs(title = "Top 10 Countries' Space Objects from 1957-2023", y = "Total Number of Objects in Space", fill = "Country") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title = element_text(size = 18),
axis.text = element_text(size = 16))
p1
# Shiny App Code
library(shiny)
library(ggplot2)
library(viridis)
# Define UI
ui <- fluidPage(
sidebarLayout(
sidebarPanel(
uiOutput("year_slider")
),
mainPanel(
plotOutput("plot")
)
)
)
# Define server
server <- function(input, output) {
df <- read.csv("top10_countries.csv")
output$year_slider <- renderUI({
sliderInput("year_range", "Select Year Range:",
min = min(df$Year), max = max(df$Year),
value = c(min(df$Year), max(df$Year)),
step = 1)
})
output$plot <- renderPlot({
# Filter data based on selected year range
filtered_df <- subset(df, Year >= input$year_range[1] & Year <= input$year_range[2])
# Plot
p <- ggplot(filtered_df, aes(x = Year, y = cum_num_objects, fill = Entity)) +
geom_bar(stat = "identity") +
scale_fill_viridis_d() +
theme_minimal() +
labs(title = paste("Top 10 Countries' Total Number of Objects in Space from", input$year_range[1], "-", input$year_range[2]),
y = "Number of Objects in Space", fill = "Country") +
theme(plot.title = element_text(hjust = 0.5, size = 22),
axis.title = element_text(size = 18),
axis.text = element_text(size = 16))
p
})
}
# Run the application
shinyApp(ui = ui, server = server)Viz 2 Code
# table(df$Entity)# Create dataframe of entities that aren't countries, regions, or 'World'
orgs <- c("APSCO", "Arabsat", "EUMETSAT", "European Space Agency", "Eutelsat", "Inmarsat", "Intelsat", "Intersputnik", "NATO", "RASCOM", "Sea Launch", "Starsem")
org_df <- df %>%
filter(Entity %in% orgs)
org_df## Entity Code Year num_objects
## 1 APSCO 2023 1
## 2 Arabsat 1985 2
## 3 Arabsat 1992 1
## 4 Arabsat 1996 2
## 5 Arabsat 1999 1
## 6 Arabsat 2006 2
## 7 Arabsat 2008 1
## 8 Arabsat 2010 2
## 9 Arabsat 2011 1
## 10 Arabsat 2015 1
## 11 Arabsat 2019 1
## 12 Arabsat 2023 1
## 13 EUMETSAT 1989 1
## 14 EUMETSAT 1991 1
## 15 EUMETSAT 1993 1
## 16 EUMETSAT 1997 1
## 17 EUMETSAT 2002 2
## 18 EUMETSAT 2005 1
## 19 EUMETSAT 2006 1
## 20 EUMETSAT 2012 2
## 21 EUMETSAT 2015 1
## 22 EUMETSAT 2018 1
## 23 EUMETSAT 2022 1
## 24 European Space Agency 1968 5
## 25 European Space Agency 1969 2
## 26 European Space Agency 1972 6
## 27 European Space Agency 1975 2
## 28 European Space Agency 1977 1
## 29 European Space Agency 1978 2
## 30 European Space Agency 1979 1
## 31 European Space Agency 1981 3
## 32 European Space Agency 1983 2
## 33 European Space Agency 1985 1
## 34 European Space Agency 1988 2
## 35 European Space Agency 1989 2
## 36 European Space Agency 1990 2
## 37 European Space Agency 1991 1
## 38 European Space Agency 1992 1
## 39 European Space Agency 1995 3
## 40 European Space Agency 1997 3
## 41 European Space Agency 1998 1
## 42 European Space Agency 1999 1
## 43 European Space Agency 2000 4
## 44 European Space Agency 2001 2
## 45 European Space Agency 2002 4
## 46 European Space Agency 2003 3
## 47 European Space Agency 2004 2
## 48 European Space Agency 2005 6
## 49 European Space Agency 2008 3
## 50 European Space Agency 2009 5
## 51 European Space Agency 2010 1
## 52 European Space Agency 2011 3
## 53 European Space Agency 2012 3
## 54 European Space Agency 2013 6
## 55 European Space Agency 2014 2
## 56 European Space Agency 2015 3
## 57 European Space Agency 2016 4
## 58 European Space Agency 2017 2
## 59 European Space Agency 2018 4
## 60 European Space Agency 2019 2
## 61 European Space Agency 2020 2
## 62 European Space Agency 2023 2
## 63 Eutelsat 1983 1
## 64 Eutelsat 1984 1
## 65 Eutelsat 1987 1
## 66 Eutelsat 1990 1
## 67 Eutelsat 1991 2
## 68 Eutelsat 1992 1
## 69 Eutelsat 1995 1
## 70 Eutelsat 1996 1
## 71 Eutelsat 1997 1
## 72 Eutelsat 1998 3
## 73 Eutelsat 1999 1
## 74 Eutelsat 2000 3
## 75 Eutelsat 2001 2
## 76 Eutelsat 2002 3
## 77 Eutelsat 2003 1
## 78 Eutelsat 2004 1
## 79 Eutelsat 2006 2
## 80 Eutelsat 2008 2
## 81 Eutelsat 2009 3
## 82 Eutelsat 2015 1
## 83 Eutelsat 2016 1
## 84 Inmarsat 1976 3
## 85 Inmarsat 1981 1
## 86 Inmarsat 1984 1
## 87 Inmarsat 1990 1
## 88 Inmarsat 1991 2
## 89 Inmarsat 1992 1
## 90 Inmarsat 1996 3
## 91 Inmarsat 1997 1
## 92 Inmarsat 1998 1
## 93 Intelsat 1965 1
## 94 Intelsat 1966 1
## 95 Intelsat 1967 3
## 96 Intelsat 1968 1
## 97 Intelsat 1969 3
## 98 Intelsat 1970 3
## 99 Intelsat 1971 2
## 100 Intelsat 1972 2
## 101 Intelsat 1973 1
## 102 Intelsat 1974 1
## 103 Intelsat 1975 2
## 104 Intelsat 1976 1
## 105 Intelsat 1978 2
## 106 Intelsat 1980 1
## 107 Intelsat 1981 1
## 108 Intelsat 1982 2
## 109 Intelsat 1983 2
## 110 Intelsat 1984 2
## 111 Intelsat 1985 3
## 112 Intelsat 1988 1
## 113 Intelsat 1989 2
## 114 Intelsat 1990 2
## 115 Intelsat 1991 2
## 116 Intelsat 1992 1
## 117 Intelsat 1993 1
## 118 Intelsat 1994 2
## 119 Intelsat 1995 3
## 120 Intelsat 1996 2
## 121 Intelsat 1997 4
## 122 Intelsat 1998 2
## 123 Intelsat 2008 1
## 124 Intelsat 2011 1
## 125 Intelsat 2012 1
## 126 Intelsat 2014 1
## 127 Intersputnik 1999 1
## 128 NATO 1970 1
## 129 NATO 1971 1
## 130 NATO 1976 1
## 131 NATO 1984 1
## 132 NATO 1991 1
## 133 RASCOM 2007 1
## 134 RASCOM 2010 1
## 135 Sea Launch 1999 1
## 136 Starsem 2000 2# Create column with cumulative number of space objects over the years
org_df <- org_df %>%
arrange(Year) %>%
group_by(Entity) %>%
mutate(cum_num_objects = cumsum(num_objects))
org_df## # A tibble: 136 × 5
## # Groups: Entity [12]
## Entity Code Year num_objects cum_num_objects
## <chr> <chr> <int> <int> <int>
## 1 Intelsat "" 1965 1 1
## 2 Intelsat "" 1966 1 2
## 3 Intelsat "" 1967 3 5
## 4 European Space Agency "" 1968 5 5
## 5 Intelsat "" 1968 1 6
## 6 European Space Agency "" 1969 2 7
## 7 Intelsat "" 1969 3 9
## 8 Intelsat "" 1970 3 12
## 9 NATO "" 1970 1 1
## 10 Intelsat "" 1971 2 14
## # ℹ 126 more rowsline_plot <- ggplot(org_df, aes(x = Year, y = cum_num_objects, color = Entity)) +
geom_point() + geom_line()
line_plot
# Removing agencies with low number of space objects / not present over several years
org_df2 <- org_df %>%
filter(! Entity %in% c("RASCOM", "Sea Launch", "Starsem", "APSCO", "Intersputnik"))line_plot2 <- ggplot(org_df2, aes(x = Year, y = cum_num_objects, color = Entity)) +
geom_point() + geom_line()
line_plot2
# Making a plotly version of the above line plot
p2colors <- viridisLite::viridis(7)
plot2 <- plot_ly(org_df2, x = ~Year, y = ~cum_num_objects, color = ~Entity, colors = p2colors,
text = ~paste(Entity, Year, ": ", cum_num_objects), hoverinfo = "text",
type = "scatter", mode = "lines+markers") %>%
# Customizing title and axis labels
layout(title = list(text = "Top 8 Agencies' Total Number of Objects in Space 1957-2023", size = 18),
xaxis = list(titlefont = list(size = 16), tickfont = list(size = 14)),
yaxis = list(title = "Number of Objects", titlefont = list(size = 16), tickfont = list(size = 14)),
showlegend = TRUE) %>%
# Customizing x-axis
layout(xaxis = list(
tickmode = "array",
tickvals = seq(1957, 2023, by = 10),
ticktext = seq(1950, 2030, by = 10)
))
plot2